Average Travel Time Research Articles

The impact on urban accessibility conditions of a proposed cableway line in the city of Manizales, Colombia

Abstract Due to the increased tendency to use private transport in urban areas of Manizales and Villamaría municipalities, it is intended to include alternative modes of transport that are more time-efficient and environmentally sustainable to improve the inhabitants’ quality of life. This article aims to analyse the inclusion impact of a sustainable public transport system, such as a new cableway line in the city connecting the Central Business District (CBD) with Ciudadela del Norte district, measuring overall average accessibility for the current and future scenario. This establishes the average travel time and the savings in terms of time shown as a percentage that these modes of transport would create in the population displacement and also which inhabitants are the most likely to benefit.

The accessibility analysis of Beijing-Tianjin-Hebei railway passenger transport network —— before and after the opening of Beijing-Zhangjiakou high-speed railway

This paper focuses on the changes in the accessibility of railway passenger transport network in the Beijing-Tianjin-Hebei region before and after the opening of the Beijing-Zhangjiakou high-speed railway. It mainly uses for models: a spatial model based on node location, cost model based on travel expenditure, model based on weighted average travel time, model based on economic potential index. These four models, combined with the required data, give the accessibility of the Beijing-Tianjin-Hebei railway passenger transport network before and after the opening of the Beijing-Zhangjiakou high-speed railway. The results of these calculations are visualized and analysed. The conclusions are as follows: (1) After the opening of the Beijing-Zhangjiakou high-speed railway, the accessibility of most cities in the Beijing-Tianjin-Hebei region is found to be enhanced. (2) Accessibility of some cities is found to be decreased in some models. (3) For different accessibility models, the degree of accessibility change of each city is different. (4) After the opening of the Beijing -Zhangjiakou high-speed railway, the accessibility distribution of Beijing-Tianjin-Hebei is still different.

Transport Emissions Modeling using Google Maps: An alternative approach for vehicle flow analysis

Continuous measurements of transport emissions are considered key issues for air pollution management in the transportation sector. In some instances, researchers may come across difficulties in doing transport emissions modeling such as overcomplexities and laborious methodologies. In this regard, the authors are introducing a novel method of doing a transport emissions modeling by utilizing Google Maps data. By getting the average travel time of a road segment and the corresponding length, the average speed can be obtained. This speed will be used to identify the flow of vehicles in terms of Passenger Car Unit (PCU) through a speed-flow curve on the basis of a Roadside Frictions Index (RSFI). PCU percentages are derived from the actual counting of vehicles using the street view features of Google Maps. Once the PCU count and the PCU percentages are established, the actual number of vehicle flow per type can now be determined. Consequently, emissions loads are calculated by multiplying the vehicle flow and road length to emissions factors derived from reliable sources.

Situation-Aware Left-Turning Connected and Automated Vehicle Operation at Signalized Intersections

One challenging aspect of the connected and automated vehicle (CAV) operation in mixed traffic is the development of a situation-awareness module for CAVs. While operating on public roads, CAVs need to assess their surroundings, especially the intentions of non-CAVs. Generally, CAVs demonstrate a defensive driving behavior, and CAVs expect other nonautonomous entities on the road will follow the traffic rules or common driving behavior. However, the presence of aggressive human drivers in the surrounding environment, who may not follow traffic rules and behave abruptly, can lead to serious safety consequences. In this article, we have addressed the CAV and non-CAV interaction by evaluating a situation-awareness module for left-turning CAV operations in an urban area. Existing literature does not consider the intent of the following vehicle for a CAV's left-turning movement, and existing CAV controllers do not assess the following non-CAV's intents. Based on our simulation study, the situation-aware CAV controller module reduces up to 47% of the abrupt braking of the following non-CAVs for scenarios with different opposing through movement compared to the base scenario with the autonomous vehicle, without considering the following vehicle's intent. The analysis shows that the average travel time reductions for the opposite through traffic volumes of 600, 800, and 1000 vehicle/hour/lane are about 58%, 52%, and 62%, respectively, considering both scenarios for the aggressive human driver following the CAV if the following vehicle's intent is considered by a CAV in making a left-turn at an intersection.

Traffic Signal Timing and Trajectory Optimization in a Mixed Autonomy Traffic Stream

This study introduces a methodology for cooperative signal timing and trajectory optimization at intersections with a mix of connected automated vehicles (CAVs) and human-driven vehicles (HVs). We represent joint signal timing and trajectory control as a mixed-integer non-linear program, which is computationally complex. The developed methodology provides a balance between computational efficiency and solution quality by (a) linearizing the nonlinear constraints and reformulating the problem with a tight convex hull of the mixed-integer solutions and (b) decomposing the intersection-level program into several lane-level programs. Hence, a unique controller jointly optimizes the trajectories of CAVs on a lane and the signal timing parameters associated with that lane. This setting will allow finding near-optimal solutions with small duality gaps for complex intersections with different demand levels. Case study results show that the proposed methodology finds solutions efficiently with at most 0.1% duality gap. We compared the developed methodology with an existing signal timing and trajectory control approach and found 13% to 41% reduction in average travel time and 1% to 31% reduction in fuel consumption under different scenarios.

Comparison of travel time for particles with different route-choice methods in two-route traffic models

We explore the route-choice problem in two-route traffic systems when real-time particle densities are provided. Four scenarios are considered, which have three types of informed particle and two types of uninformed particle. They adopt different route-choice rules. The system is mimicked by a lattice model and each vehicle is supposed as a hard-core “particle”. With a macroscopic approach, the stability conditions of systems are theoretically analyzed, which separate long, intermediate and short travel time phases. The Monte Carlo simulation results certify theoretical predictions. The relative benefits of route-choice rules are compared by average travel times of different types of particles, which depend on system’s phases. These results are useful for traffic flow optimization on traffic networks.

Integrating computer vision and traffic modeling for near-real-time signal timing optimization of multiple intersections

Adaptive signal timing optimizations can improve the efficiency of road networks and reduce the emissions of pollutants, but most of the current studies still rely on simplified analytical methods to depict complex road transport systems and focus on optimizing traffic signals at an isolated intersection. A framework that integrates computer vision and traffic modeling is proposed to link the real-world transport systems and operable virtual traffic models for the signal timing optimization at multiple intersections. The integrative framework consists of six main steps, including configuring real-time video sources, conducting transfer-learning to develop the vehicle detector, comparing and selecting vehicle trackers, collecting traffic parameters by referring to the CV-TM ontology, establishing and running the traffic model, and operating simulation-based optimizations. The proposed integrative framework is demonstrated through a case study of the signal timing optimization at multi-intersections in a real-world road network. Three critical information items including the traffic volumes, vehicle compositions, and vehicles’ turning ratios are derived from real-time surveillance videos, and the extracted information is then automatically incorporated into TM to optimize the signal timings of interconnected intersections in a near-real-time manner. In comparison with the original signal scheme, the optimized one can reduce 14.2 % of average vehicle delays, 18.9 % of vehicle stops, 9.1 % of average travel time, and 2.3 % of pollutant emissions in this specific case. The results indicate that synchronously optimizing signal timings at multiple intersections increase not only the transportation efficiency but also the environmental friendliness of road transport systems. The proposed CV-TM integration framework is demonstrated to be a promising way for conducting near-real-time signal timing optimizations in intricate traffic scenes instead of at isolated intersections, helping decision-makers to promptly respond to the time-varying traffic conditions during various real-world events, and facilitating the transportation systems and cities to achieve sustainable development goals.

The effect of modern traffic information on Braess’ paradox

Braess’ paradox has been shown to appear rather generically in many systems of transport on networks. It is especially relevant for vehicular traffic where it shows that in certain situations building a new road in an urban or highway network can lead to increased average travel times for all users. Here we address the question whether this changes if the drivers (agents) have access to traffic information as available for modern traffic networks, i.e. through navigation apps and or personal experiences in the past. We study the effect of traffic information in the classical Braess network, but using a microscopic model for the traffic dynamics, to find out if the paradox can really be observed in such a scenario or if it only exists in some theoretically available user optima that are never realized by drivers that base their route choice decisions intelligently upon realistic traffic information. We address this question for different splits of the two information types.

Evacuation based on spatio-temporal resilience with variable traffic demand

The efficient evacuation of people from dangerous areas is a key objective of emergency management. However, many emergencies give little to no advanced warning, leading to spontaneous evacuation with no time for planning or management. For large emergencies, destinations become less certain, with traffic demand imbalanced and concentrated on a few oversaturated routes familiar to evacuees. Ultimately, this leads to rapid congestion and delay on some routes, while others remain barely used, extending clearance times with an accumulating population at risk. In this study we address these issues through incorporating spatio-temporal traffic resilience dynamics into a destination choice model utilizing the available capacity of the overall network. We validate our model through a post-concert egress event. The results suggest that our method can reduce total egress times and average travel time by 20%–43% over the no-guidance condition. Our method can be used to estimate and quantify emergency conditions to optimally guide destinations and routing choice for evacuees and/or autonomously moving vehicles during evacuations.

MVCM Car-Following Model for Connected Vehicles and Simulation-Based Traffic Analysis in Mixed Traffic Flow

Although there have been assorted car-following (CF) models for connected vehicles (CVs), studying their impacts in mixed traffic flow of human-driven vehicles (HVs) and CVs remains a challenge. Considering the multiple front vehicles’ optimal speed changes with memory, this study proposed a new CF model (MVCM model) implemented through vehicle-to-everything (V2X) technology in CVs environment in terms of OVCM (Optimal Velocity Changes with Driving Memory) model. The stability condition of MVCM model was derived through linear stability analysis. Then, the disturbance propagation of MVCM model was compared with that of both classical FVD (Full Velocity Difference) model and MHOVA (Multiple Headway Optimal Velocity and Acceleration) model. Finally, a case study was conducted in VISSIM to analyze the impact of CV (with MVCM model) rates on traffic characteristics, including the average speed, delay time and travel time. Results show that 1) considering more front vehicles’ optimal speed strengthens the stability of traffic flow and the optimal considered vehicle number is 4; 2) MVCM model shows better resistance to disturbance than FVD model; 3) and obtain the same stability with MHOVA model, but with fewer front vehicles considered (only 3); 4) larger rate of CVs leads to higher average speed, smaller average travel time and the delay time of CVs and HVs; and 5) CVs’ positive effects reaches stable when CV rate approaches 0.6.

Robust Optimization of Bus Stop Placement Based on Dynamic Demand Using Meta Heuristic Approaches: A Case Study in a Developing Country

Abstract The operating condition of bus transit system has not been efficient in most cities of Iran, and many management methods such as regular bus scheduling, assigning exclusive bus lanes, etc., which are necessary for increasing the efficiency of this system, were not regarded enough. Thus, achieving a method for locating the bus stops and optimizing the number of such stops based on a non-homogeneous spatial and temporal distribution of passengers as well as the local traffic patterns are important to be investigated. As such, the present study aims to investigate the modeling of a bus transit system corridor according to the non-homogeneous spatial and temporal distribution of passengers throughout the route aiming at optimization of the number of attracted passengers to the bus. For this purpose, the 8-km route from Vali-e-asr roundabout to Gas roundabout in the city of Rasht in the north of Iran is selected for modeling. Hammersley sampling method, as well as two heuristic optimization techniques, including a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm, are used for generating a non-uniform population and solving the optimization model. Therefore, the results of this analysis are compared to the optimization results by using the probabilistic analysis without considering the reference uncertainty. Finally, the PSO is selected as the superior algorithm for modeling and locating the bus stops due to its results in less travel time, and the validity of robust optimization model is shown due to its higher accuracy and adaptation to the real-world environment. Overall, although the optimization results based on indeterminate analysis in comparison to determinate analysis brought about more average travel time, more population sets were covered by the new introduced stops during 18 active hours of the bus transit system.

Freeway Short-Term Travel Time Prediction Based on LightGBM Algorithm

In order to realize the prediction of freeway travel time, a short-term travel time prediction model based on LightGBM (Light Gradient Boosting Machine) is proposed under the influence of weather factors, time period factors, and traffic factors. These factors are called as the features used for increase prediction accuracy. The travel time of a single vehicle is determined by license plate recognition data of two adjacent video monitors in Shaoxing section of Shanghai-Hangzhou-Ningbo Freeway, and a better travel time data set is constructed by data preprocessing. The feature data are determined by Pearson correlation. Based on the analysis of main optimization parameters in LightGBM, the short-term average travel time is predicted, the MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) obtained by experiments are satisfying, indicating that LightGBM model has high accuracy and good fit. Finally, through comparison with KNN (K-Nearest Neighbor) model and GBDT (Gradient Boosting Decision Tree) model, the prediction accuracy and training speed both show that LightGBM has good advantages in predicting short-term freeway travel time.

Simulation Study of Dynamic Bus Lane Concept

Not many publications are available on using microsimulation models to analyze the feasibility of implementing the dynamic bus lane (DBL) concept. The paper presents the methodology and calibration process used for DBL modeling. For the selected four sites in Rzeszów (Poland), three options were analyzed: no bus lane, standard exclusive bus lane (XBL), and dynamic bus lane. The analyses were carried out using PTV Vissim software with an additional logic script to control the DBL activation. Simulation model parameters were calibrated using a genetic algorithm. The final assessment of individual options was based on the weighted average travel time for all transport modes. The results show that the dynamic bus lane could bring the same benefits to public transport and cause only a slight increase in travel times in private transport compared to XBL. The XBL solution, depending on the site, led to increasing the average travel time in private transport by 12% to 25%, while the dynamic bus lane increased by 1% to 12%. Weighted average travel time per person is proposed as the overall indicator of efficiency. Preliminary analyses show that the bus volume, bus occupancy, routing, and traffic conditions will affect the efficiency of the new solution.

Modeling relationships between the network distance and travel time dynamics for assessing equity of accessibility to urban parks

ABSTRACT Static models of accessibility are usually based on the fixed distance or Average Travel Time (ATT) models. Because of ignoring the traffic as a dynamic process affecting the accessibility through the change of Travel Time (TT), these models lead to unperceived temporal inequities. In contrast to the consideration of the temporal Variation of TT (VTT) in the previous studies, the variation of traffic-related TT and its relations with network distance has not been considered. In this study, relations between VTT and network distance to access urban parks in Tehran megacity has been modeled. Traffic maps at five times of day are used to produce TT maps of Traffic Analysis Zones (TAZs) to their 3-closest parks. Comparison of the Gini coefficients of accessibility show significant inequities of accessibility at different times of day. Relations between the distance, ATT, and TT max are modeled by statistical analysis. Results show both TT and TT max have significant positive relations with distance and traffic and reach their maximum at 6 p.m. Observation of significant relations between distance, ATT, TT max, and VTT provides interesting knowledge for the conversion of temporal measures of equity (TT) to a physical measure of equity (distance). A simple application of these findings for effective management of the spatiotemporal inequities is the definition of critical distances from public services. As an example, to decrease the TT max of TAZs to less than 12 min, their maximum distance to the closest parks should be less than 4 km. The developed approach can be adopted for the accessibility evaluation of the other public services, particularly the health and education centers.

Tele-TB: Using TeleMedicine to Increase Access to Directly Observed Therapy for Latent Tuberculosis Infection.

Treatment of latent tuberculosis infection (LTBI) decreases risk of progression to active tuberculosis. Traditional treatment regimens required either daily isoniazid for 9 months, with historically poor compliance, or 12-week directly observed therapy (DOT) with isoniazid and rifapentine, with improved compliance but additional challenges of coordinating weekly clinic visits, further complicated if patients must travel a great distance for care. Our referral area is complicated by congested traffic often resulting in one-way commutes, which can exceed 2 hours. These travel times would be prohibitive for conducting weekly in-clinic DOT. In an effort to improve access to DOT, we implemented TeleMedicine LTBI DOT (vDOT) within a military pediatric infectious diseases clinic. Patients aged 24 months or older diagnosed with LTBI were referred for possible enrollment into our vDOT clinic. All patients without contraindications for receiving isoniazid and/or rifapentine were offered LTBI treatment via weekly vDOT or daily treatment with isoniazid or rifampin. The first visit for vDOT patients was performed in person to discuss treatment options, demonstrate use of TeleMedicine software, and ensure the patient was able to take the medications. Baseline information about patients and travel time to our facility was determined. To date, 16 patients have completed LTBI therapy using vDOT. Average one-way travel time to our facility for patients was 51 minutes. Actual time spent in most vDOT encounters was less than 10 minutes. Appointments were arranged to take place outside usual school and work hours so patients could complete vDOT with minimal interruptions to daily life, resulting in 100% treatment compliance and completion. Conducting LTBI DOT using TeleMedicine is a viable and time-saving measure that still allows for high levels of patient compliance and treatment completion while minimizing interruptions to academic and work schedules.

The benefits of remoteness – digital mobility data, regional road infrastructure, and COVID-19 infections

Abstract We investigate the regional distribution of the COVID-19 outbreak in Germany. We use a novel digital mobility dataset, that traces the undertaken trips on Easter Sunday 2020 and instrument them with regional accessibility as measured by the regional road infrastructure of Germany’s 401 NUTS III regions. We identify a robust negative association between the number of infected cases per capita and average travel time on roads to the next major urban center. What has been a hinderance for economic performance in good economic times, appears to be a benevolent factor in the COVID-19 pandemic: bad road infrastructure. Using road infrastructure as an instrument for mobility reductions we assess the causal effect of mobility reductions on infections. The study shows that keeping mobility of people low is a main factor to reduce infections. Aggregating over all regions, our results suggest that there would have been about 55,600 infections less on May 5th, 2020, if mobility at the onset of the disease were 10 percent lower.

Strategic Information Perturbation for an Online In-Vehicle Coordinated Routing Mechanism for Connected Vehicles Under Mixed-Strategy Congestion Game

The increased market penetration of route guidance tools–relaying real-time traffic information to drivers–can have damaging effects on transportation networks, including traffic congestion oscillation resulting from the overreaction phenomenon and the inability to control system performance. To address these issues, this study leverages V2I communication capabilities to integrate strategic real-time traffic information perturbation into an online, in-vehicle coordinated routing mechanism for connected vehicles using a mixed-strategy congestion game (CRM-M-IP). Under the CRM-M-IP, the routing decisions of all vehicles are coordinated to prevent overreaction. Additionally, the routing decisions for all vehicles are based on strategically perturbed traffic information (a convex combination between average and marginal link travel times), to ensure that the selfish route choices made by users also help improve system performance. We prove that low information perturbation levels can lead to high system performance gains with correspondingly low individual user optimality losses. From numerical experiments conducted on the Sioux Falls network, we observe that the CRM-M-IP leads to a system performance improvement greater than 3%, and average individual travel time reduction up to 3.5% as compared to the case with no perturbation. Moreover, we find that the average individual user optimality loss resulting from information perturbation is less than 2%. However, we find that when perturbation is high, some users can experience losses approaching 30%—illustrating the need to not over-perturb to ensure compliance of drivers.

Adaptive Traffic Signal Control: Game-Theoretic Decentralized vs. Centralized Perimeter Control.

This paper compares the operation of a decentralized Nash bargaining traffic signal controller (DNB) to the operation of state-of-the-art adaptive and gating traffic signal control. Perimeter control (gating), based on the network fundamental diagram (NFD), was applied on the borders of a protected urban network (PN) to prevent and/or disperse traffic congestion. The operation of gating control and local adaptive controllers was compared to the operation of the developed DNB traffic signal controller. The controllers were implemented and their performance assessed on a grid network in the INTEGRATION microscopic simulation software. The results show that the DNB controller, although not designed to solve perimeter control problems, successfully prevents congestion from building inside the PN and improves the performance of the entire network. Specifically, the DNB controller outperforms both gating and non-gating controllers, with reductions in the average travel time ranging between and , total delay ranging between and , and emission levels/fuel consumption ranging between and . The results demonstrate statistically significant benefits of using the developed DNB controller over other state-of-the-art centralized and decentralized gating/adaptive traffic signal controllers.

SCP-Tree: Finding Multiple Nearest Parking Spots With Minimal Group Travel Cost

Finding the nearest parking location in road networks is one of the most commonly faced challenges in everyday life of green transportation. A main challenge faced by the state-of-the-art existing parking allocation methods is to optimally offer the nearest parking location for a group of m users at the cost of minimal overall traveling time to ensure the traffic and environmental sustainability. In this article, we model it as a Multiple Nearest Parking Location Allocation (MNPLA) problem, and devise a spatial index tree, called SCP-tree, to accelerate the nearest parking location allocation within the users’ time constraints. During the search process in SCP-tree, we build a pruning strategy relevant to the Geographical Preference Estimation, travel time and parking capacity to determine which branch to visit so that the search accuracy can be improved. Considering the users’ behaviors are often impacted by the geographical location and some personalized attribute information, we set the user priority based on them to help the parking officer determine the allocation sequence. We evaluate our allocation scheme using large real-world dataset with on-street parking sensor data, and extensive experimental results reveal (i) a minimum improvement of 15.9%, 1.4%, 96.9%, 160% in parking allocation time, average traveling time, I/O cost and service utility compared to the progressive methods, and (ii) a minimum improvement of 8.9%, 11.1%, 78.2%, 714% in parking allocation time, average traveling time, I/O cost and service utility compared to the baseline methods.

A Novel Approach for Dynamic Traffic Assignment Based on Multi-Agent Node Reservation: Comparative Study on Two Competing Roads

This paper aims to contribute to the challenging issue of one microscopic simulation round for dynamic traffic assignment. It relays on the selfish behavior of the vehicle agent that benefits from a more accurate estimation of its travel time. The main novelty is that, rather than considering the average travel times in the network links according to the present vehicles, the vehicle must first know at what time it is able to cross the nodes located at both extremities of the road. This is achieved through a negotiation between the vehicle agent and the node agents to book the crossing time. In order to assess this new paradigm, this paper compares it with well-known approaches in an elementary network. The result invites to extend the approach to more general cases. A discussion of the opportunities and limitations of the approach extension is provided in this paper. One of the notable opportunities, is that the proposed approach has a great potential to improve the energy efficiency by exploring mobile navigation applications and connected autonomous vehicles.